Detection and analysis of target objects in digital images are useful and important tasks. For example, detection and diagnosis of abnormal anatomical regions in radiographs, such as masses and microcalcifications in womens' breast radiographs (mammograms), are among the most important and difficult tasks performed by radiologists.
Breast cancer is a leading cause of premature death in women over forty years old. Evidence shows that early detection, diagnosis and treatment of breast cancer significantly improves the chances of survival, reducing breast cancer morbidity and mortality. Many methods for early detection of breast cancer have been studied and tested, among them mammography. To date mammography has proven to be the most cost effective means of providing useful information to diagnosticians regarding abnormal features in the breast and potential risks of developing breast cancer in large populations. The American Cancer Society currently recommends the use of periodic mammography and screening of asymptomatic women over the age of forty with annual examinations after the age of fifty. Mammograms may eventually constitute one of the highest volume X-ray images routinely interpreted by radiologists.
Between thirty and fifty percent of breast cancers detected radiographically demonstrate clustered microcalcifications on mammograms, and between sixty and eighty percent of breast cancers reveal microcalcifications upon microscopic examination. Therefore, any increase in the detection of clustered microcalcifications by mammography may lead to further improvements in its efficiency in the detection of early breast cancer.
Currently acceptable standards of clinical care are that biopsies are typically performed on five to ten women for each cancer removed. With this high biopsy rate is the reasonable assurance that most mammographically detectable early cancers will be resected. However, reducing the biopsy rate without adversely affecting health is desirable. Accordingly, given the large amount of overlap between the characteristics of benign and malignant lesions which appear in mammograms, computer-aided detection (CAD) of abnormalities may have a great impact on clinical care.
At present, mammogram readings are performed visually by mammographic experts, that is, physicians and radiologists. Unfortunately, visual reading of mammograms has two major disadvantages. First, it is often possible to miss the breast cancer in its early stages. This is because, unlike many other cancers, there is as yet no clear way to detect premalignant changes in the breast. This results partly from the relative inaccessibility of breast tissue. A second disadvantage of visual reading of mammograms is that these readings are both labor intensive, time consuming and subjective. Multiple readings of a single mammogram may be necessary in order to increase the reliability of the diagnosis.
Therefore, it would be advantageous and useful to have computer-assisted or aided detection (CAD) systems to help radiologists and physicians obtain quicker, more consistent and more precise results when performing visual readings of mammograms. Such CAD systems would aid in cancer detection and improve the efficiency and accuracy of large-scale screening.
Various computer-assisted detection systems have been investigated to assist diagnosticians in their diagnosis of breast cancer. To date, research in computerized detection systems for masses and microcalcifications in digital mammograms has largely been based on thresholding methods or neural networks.
Grey-scale morphology has been used in a variety of ways to identify suspicious regions depicted in projection radiographs. ("Morphology" is the study of form and shape.) These prior techniques have been used in conjunction with chest radiographs and mammograms to aid in the identification and classification of normal and abnormal tissues such as solid masses and clustered microcalcifications depicted in those radiographs and mammograms.
Grey-scale morphology-based detection and identification techniques use a gradient or change in a specific single characteristic or variable of an image to identify abnormal tissue depicted in radiographs. These changes are linearly thresholded to discriminate between normal and abnormal tissue, the threshold being determined based on known cases. For example, some techniques use changes in size or shape factors of a particular region as a function of digital value (contrast) to identify abnormal tissue. For a particular region, changes above some linear threshold are considered to indicate abnormal tissue in that region, whereas changes below the threshold are considered to indicate that the region depicts normal tissue.
Other techniques using grey-scale morphology have been based on a feature analysis at the so-called "transition" or "crossover" layer. This transition layer is the growth layer at which a particular feature begins growing out of the background tissue.
These prior techniques first find the transition layer for a suspected abnormal region and then apply a is feature analysis to the image at that layer.
For example, one prior art system takes the approach that by using two variables (namely size growth factor and circularity) at the transition layer and grey-scale morphology, the base of any suspicious region can be determined where the region grows very fast. Then, at only one grey level just above the base or cross-over, a limited set of five or six measures or characteristics are computed. Using linear thresholding only at that single level, a determination is made as to whether or not the region is abnormal.
The use of features from only one level to analyze a growth area, especially from the cross-over level, yields unsatisfactory results since this layer contains more information on background or surrounding regions than on the growth area itself.
Gur disclosed a method and apparatus for detecting abnormal regions in living tissue depicted in a radiograph. Gur finds suspected regions and then uses several top view layers of each suspected region to determine whether or not that region is an abnormal region. A rule-based selection of changes in features is used to select and evaluate suspicious regions.
In Gur, a digitized radiograph is subjected to two stages of processing. In the first or identifying stage, a set of suspicious regions are found in the digitized radiograph. This set of suspicious regions contains some regions that may not contain actual abnormal regions. That is, suspected abnormal regions are identified in the digital radiograph. The second or pruning stage removes false-positive suspected regions found in that first stage. That is, the pruning stage removes suspected regions that are not actual abnormal regions. In the pruning stage, for each identified suspected abnormal region that was found in the identifying stage, multiple topographic layers of that region are extracted from the digitized radiograph and are evaluated to determine whether the suspected region is an actual abnormal region.
Various features of the digitized radiograph are determined for each suspected region. These features are then analyzed and compared to predetermined criteria to determine whether a suspected region is an actual abnormal region. Preferably, at least two adjacent, top-view topographic layers are used.
The features determined and compared include the size, digital value contrast, shape factor and digital value fluctuation of each suspected abnormal region. Gur may include a generated rule-based criteria database, for use in evaluating suspected regions.
Gur's approach achieves good results but these can be improved by the methods and mechanisms described herein.